Papers

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

In this paper, we construct a one-dimensional photonic quasicrystal by combining two incommensurate spatial harmonics, where the ratio of their periods is the irrational number \beta. We evaluate the photonic quasicrystal accurately by a generalized spectral method that embeds the quasiperiodic structure into a higher-dimensional periodic system. We study the spectral distribution of one-dimensional photonic quasicrystals and find some interesting phenomena. As the computational resolution N increases, there are more eigenvalues within finite frequency bandwidths, and the maximum localization always occurs at spectral gap edges for states near index N + 1. By varying \beta within the range of (0,1), we present a butterfly-shaped spectral structure with abundant band gaps. We find that the spectral structure factor Q (defined as I_{mg}/N, where I_{mg} is the maximum gap index) exhibits different linear patterns as \beta changes: Q = 1 - \beta when \beta < \beta c, while Q = \beta when \beta > \beta c, where \beta c \approx 0.424 is the transition point. This linear relationship holds robustly in the strong quasiperiodic regime (\beta away from 0 or 1) and is independent of the specific type of irrational number used. The relationship disappears (weak quasiperiodic regime) near \beta = 0 or \beta = 1. It demonstrates that the intrinsic spectral properties of one-dimensional photonic quasicrystals are fundamentally governed by the magnitude of the irrational parameter \beta.

Rendering reflections in curved mirrors is crucial for enhancing the realism in computer-generated hologram (CGH), yet it poses a fundamental challenge due to the unique computational principles of CGH. Conventional methods using Bézier clipping are computationally prohibitive, and a previously proposed mirror surface subdivision method suffered from the computation time increasing with mirror curvature. To address these limitations, this paper proposes a novel calculation method based on Fermat's principle that directly and efficiently determines the reflection point by minimizing the optical path length from a point light source to a hologram pixel via the mirror surface, using Newton's method for optimization. Experimental results demonstrate that this method significantly reduces computation time compared to previous approaches. Furthermore, it enables the rendering of multiple reflections from several mirrors, a capability that was challenging for conventional techniques.

We present a vector network analyzer (VNA) based broadband cryogenic ferromagnetic resonance (FMR) spectrometer, operating up to 20 GHz over a temperature range of 11 to 350 K. A cost effective architecture is implemented through the integration of a closed cycle refrigerator (CCR) and a custom fabricated grounded coplanar waveguide (GCPW), designed for broadband transmission and reliable cryogenic operation. The VNA calibration is performed prior to measurements to account for microwave background and transmission losses, enabling reliable extraction of FMR spectra across the full temperature / frequency range. The sensitivity of the spectrometer is benchmarked using a yttrium iron garnet (YIG) thin film, yielding well resolved resonances with narrow linewidths and high sensitivity.

Ultrathin multimode fibers (MMFs) promise endoscopes with hair-scale diameters for accessing sub-millimeter anatomy, but in MMF far-field imaging the required small collection aperture drives speckle-dominated measurements that rapidly degrade image fidelity. Here we present Speckle Clean Network (SCNet), a physics-guided foundation model for universal speckle removal that makes photon-limited, single-fiber collection compatible with high-fidelity reconstruction across diverse scattering conditions without target-specific retraining. SCNet combines a Mixture of Experts (MoE) architecture with material-aware routing, wavelet-based frequency decomposition to separate structure from speckle across sub-bands, and a curriculum-style optimization that progressively enforces spectral consistency before spatial fidelity. Using an ultrathin dual-fiber holographic probe, we deliver wavefront-shaped illumination through one MMF and collect backscattered photons through a parallel MMF. We validate SCNet on 3D plastic objects over varying working distances, resolve 5.66 lp/mm on a paper USAF target, and restore fine structures on leaves and metal surfaces. On rabbit heart and kidney tissues, SCNet improves recovery of low-contrast anatomical texture under the same ultrathin collection constraint. We further compress SCNet through multi-teacher distillation to reduce computation while preserving reconstruction quality, enabling inference at 60 FPS. This work effectively decouples image quality from probe size, establishing a speckle-free ultrathin endoscopy for stand-off imaging in confined spaces.

High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as constrained black-box searches over rugged objective landscapes, where function evaluations are expensive, and gradients are unavailable or unreliable. Conventional global search engines and optimizers struggle in such settings due to the exponential scaling of design spaces, the presence of multiple local basins, and the absence of physical guidance in sampling. We present a physics-informed Monte Carlo Tree Search (MCTS) framework that extends policy-driven tree-based reinforcement concepts to continuous, high-dimensional scientific optimization. Our method integrates population-level decision trees with surrogate-guided directional sampling, reward shaping, and hierarchical switching between global exploration and local exploitation. These ingredients allow efficient traversal of non-convex, multimodal landscapes where physically meaningful optima are sparse. We benchmark our approach against standard global optimization baselines on a suite of canonical test functions, demonstrating superior or comparable performance in terms of convergence, robustness, and generalization. Beyond synthetic tests, we demonstrate physics-consistent applicability to (i) crystal structure optimization from clusters to bulk, (ii) fitting of classical interatomic potentials, and (iii) constrained engineering design problems. Across all cases, the method converges with high fidelity and evaluation efficiency while preserving physical constraints. Overall, our work establishes physics-informed tree search as a scalable and interpretable paradigm for computational design and high-dimensional scientific optimization, bridging discrete decision-making frameworks with continuous search in scientific design workflows.

We consider a response system that updates its internal state in accordance with information input arriving from outside. In this paper, we define as internal time the “number of kinds” of codes that have been observed at least once up to a given time, and analyze how the way internal time advances is determined by the statistics of information input (arrival rate and code distribution). When arrivals follow a Poisson process, the average advancing speed of internal time decreases monotonically with time, and if the number of kinds of codes is finite, it eventually approaches an upper limit and saturates. As a result, on long time scales, internal time becomes relatively shorter than physical time. For a uniform code distribution, we provide a closed form for the correspondence between internal time and physical time, and show that the physical time required to “advance internal time by one step” increases in later stages. As an ancillary quantity, we quantify by conditional entropy the remaining uncertainty of “which codes have been observed” when only internal time is known, and we give unimodality and the maximization time in the uniform case, and upper bounds, equality conditions, and expressions of the difference from the upper bound in the non-uniform case. Finally, we also present a generalization that assigns weights (description lengths) to each code so that internal time is ticked according to the amount of information in the input.

Global warming is projected to intensify the hydrological cycle, amplifying risks to ecosystems and society. While extreme rainfall appears to exhibit stronger sensitivity to global warming compared to mean rainfall rates, a unifying physical mechanism capable of explaining this systematic divergence has remained elusive. Here, we integrate theory and data from a global network of nearly 50,000 rain-gauge stations to unravel the rainfall intensity and frequency response to rising temperatures. We show that the distributions of wet-day rainfall depth exhibit self-similar shapes across diverse geographical regions and time periods. Combined with the temperature response of rainfall frequency, this consistently links mean and extreme precipitation at both local and global scales. We find that the most probable change in rainfall intensity follows Clausius-Clapeyron (CC) scaling with variations shaped by a fundamental hydrological constraint. This behavior reflects a dynamic intensification of updrafts in space and time, which produces localized heavy precipitation events enhancing atmospheric moisture depletion and hydrologic losses through runoff and percolation. The resulting reduction in evaporative fluxes slows the replenishment of atmospheric moisture, giving rise to the observed trade-off between rainfall frequency and intensity. These robust scaling laws for rainfall shifts with temperature are essential for climate projection and adaptation planning.

Light driven oxygen formation in Photosystem II protein is a fundamental process that sustains our biosphere and serves as a blue print to future clean energy solutions due to its high energy conversion efficiency. Last decade of intense research by advanced physical techniques delivered new insights on the structure and function of the Mn4CaO5 cluster a center of the oxygen evolving complex (OEC). However, discrepancies in experimental observations and computational models persist impeding the understanding of the O-O bond formation and the role of the protein environment in the process. Here we show that i) assignment of the OEC unique oxygen O3 ligated by histidine (His337) via dynamic H-bond as a slow exchanging substrate and ii) its coupling with O6 oxygen generated at Mn1 in the S2 to S3 transition give the O-O bond formation mechanism most consistent with all currently available experimental data. Proposal shows how protein environment can steer the O-O bond formation by charge control via H-bond and open coordination of Mn1. Obtained O3-O6 peroxide is at lower energy than peroxides in the most studied O5-O6 bond formation pathway. His337 appears to be similar to distal His in globins used for management of the O2 and H2O2 intermediates. The new mechanism breaks the prior impasse and will undoubtedly invigorate future detailed studies uncovering its further details.

We propose a novel level set-based topology optimization for micropolar solids subjected to thermo-mechanical loading. To capture the size effects, we have incorporated the microstructural length-scale information into the level set-based topology optimization method by adopting a micropolar theory. The proposed non-local topology optimization method can provide accurate topology optimization for size-dependent solids under thermo-mechanical loading. We have demonstrated the effectiveness of the proposed method through a few representative two-dimensional benchmark problems. The numerical results reveal the substantial influence of underlying micro-structures, incorporated in the model through micropolar parameters, and temperature on topology optimization, highlighting the necessity of the proposed thermo-mechanical micropolar formulation for materials with pronounced non-local effects. For the numerical implementation of the proposed model, we have used open-source finite element libraries, Gridap.jl, and GridapTopOpt.jl, available in Julia, to ensure transparency and reproducibility of the reported computational results.

This study investigates the causal timeline of vortex stretching in high-Reynolds-number turbulence ($Re_\lambda \approx 433$) using Lagrangian tracking in $1024^3$ direct numerical simulations. While classical theories often assume an instantaneous alignment between strain and vorticity, the present analysis identifies a systematic Lagrangian phase lag governing the onset of intense dissipation. By conditionally averaging the dynamics of fluid parcels, a distinct phase-space hysteresis is revealed. Trajectories are captured by the saddle-point topology of the pressure field ($\lambda_{min}^p < 0$) prior to experiencing peak enstrophy amplification. This temporal ordering ($\tau > 0$) demonstrates that the pressure topology acts as a deterministic geometric precursor, organizing the flow structure before the bursting event occurs. The robustness of this mechanism is verified in magnetohydrodynamic (MHD) turbulence, where the Lorentz force is found to suppress the hysteresis loop, forcing a transition from causal precedence to simultaneous locking.

Since decades, the CEA has been involved in the development of 4th generation reactors cooled by sodium. The dedicated experimental platform PLATEAU was erected to study the different issues and built a results database for the code validation. As experiments with sodium are complex, the tests are led in mock-ups using water as a simulant fluid. The MICAS mock-up, model of the upper plenum, aims at studying the gas entrainment at the free surface. Gas is prohibited in the reactor core due to neutron effects, which may induce reactivity variations. Since vortices are difficult to handle by numerical codes in complex geometries such as reactor ones, criteria have been developed based on the Rankine vortex model to forecast the air entrainment. However, in some case, numerical results do not correlate the experimental ones and those discrepancies are may be due to the vortex model. This article aims at comparing the vortices characteristics calculated using the Rankine vortex model with experimental results in order to improve the gas entrainment criteria. The parameters of the Rankine model are determined using the velocity fields measured by the Particle Image Velocity (PIV) method and the vortices diameter is calculated using the raw PIV images.

In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps conditioned on key detector and beam parameters. The model employs efficient inference using Denoising Diffusion Implicit Model sampling and is pre-trained on GEANT4 simulations before being adapted to a new calorimeter geometry through Low-Rank Adaptation, requiring only a small post-training dataset. We evaluate physically meaningful observables, including total deposited energy, energy-weighted radius, and shower dispersion, obtaining relative root mean square error values below 2% for representative high-energy cases. This is in line with state-of-the-art calorimeter surrogates which report comparable fidelity on high-level observables. Furthermore, we compare gradients of a reconstruction-based utility function with respect to design parameters between the surrogate and finite-difference references. The diffusion surrogate reproduces the qualitative structure and directional trends of the true utility landscape, providing usable sensitivities for gradient-based optimization. These results show that diffusion-based surrogates can accelerate simulation-driven detector design while enabling differentiable, gradient-informed analysis.

We construct an explicit algebraic realisation of three fermion generations within a single Clifford algebra, transforming under the full Standard Model $SU(3)_C\times SU(2)_L\times U(1)_Y$ gauge group, in which an intrinsic $S_3$ family symmetry permutes three algebraically distinguished but gauge-equivalent fermion sectors without replicating the gauge bosons. Fermionic states are represented by minimal left ideals of the complex Clifford algebra $\mathbb{C}\ell(10)$, while the three-generation structure arises from an embedded discrete $S_3$ symmetry acting on the space of algebraic spinors. The Standard Model gauge generators are identified as elements commuting with this $S_3$ action and act on physical states via the adjoint (commutator) action. The resulting spectrum reproduces the correct Standard Model quantum numbers for three linearly independent generations of fermions.

Certain approaches to quantum gravity, such as the one based on the concept of purely virtual particles (fakeons), sacrifice the cause-effect relation at very small scales to reconcile renormalizability with unitarity. Other developments have also urged caution regarding the idea of causality as a fundamental principle. In this paper, we examine the problem from multiple perspectives, including locality and predictivity, and extend the existing skepticism in several directions. Emphasizing the impact of unruly "disruptors", we point out that the illusory arrow of time associated with causality and predictivity is inherently statistical. This renders the cause-effect relation strained at the microscopic level. We also show that causation is a borderline concept that demands belief in entities which can act on nature without being part of it. Ultimately, not only is renouncing microcausality a reasonable price to pay for a consistent and predictive theory of quantum gravity (as is the one based on the fakeon idea), but the very notion of causality is misleading. Resting as it does on metaphysical assumptions, it should therefore be abandoned in fundamental physics.

Rydberg atoms represent a platform underpinning many recent developments in quantum computation, simulation, sensing, and metrology. They further facilitate optical nonlinearity at the single-photon level when coupled to photons propagating in atomic clouds, which form collective atomic excitations called Rydberg polaritons, strongly interacting with each other. Here, we experimentally explore interactions between a Rydberg polariton in an atomic ensemble and a single, adjacent, Rydberg atom. We discover three different regimes of quantum dynamics corresponding to polariton blockade, coherent exchange, and probabilistic hopping, which are defined by their distinct transmission characteristics, with a transition through an exceptional point occurring between blockade and coherent exchange. We investigate the applications of such interactions for fast, non-destructive detection of Rydberg atoms and present proof-of-principle demonstrations for their potential application in nonlinear photonic networks.

We demonstrate the use of the Fast Fourier Transform Beam Propagation Method (FFT BPM) to simulate dynamic diffraction effects, including scattering from deformed crystals with arbitrary shapes in Bragg, Laue, and asymmetric geometries. The method's straightforward algorithm, combined with FFT, enables fast computation and is easy to implement in Python. It successfully reproduces literature results for bent crystals, dislocations, and finite-shaped crystals simulated using the Takagi-Taupin equations. Python implementations for each case are provided in a public GitHub repository, with the code structured for parallel computing.

Metamaterials are arrangement of basic building blocks that repeat in space, time, or both. These material systems serve as an excellent platform for controlling waves, such as engineering wavenumber band gaps, flat bands, and zero-frequency band gaps. However, combining one or more of these exotic features within the same unit cell design remains a challenge. Moreover, once a metamaterial is realized, its dispersive properties are usually fixed. In this work, we present a tunable passive hierarchical metamaterial capable of exhibiting wavenumber band gaps, flat bands, and zero-frequency band gaps within the same dispersion curve. Our metamaterial is composed of magnetic elements confined within a fixed magnetic boundary. The metamaterial can be tuned by adjusting the magnetic boundary, which in turn can alter the lattice periodicity. We open wavenumber band gaps by incorporating magnetic coupling within the unit cell elements, resulting in negative physical stiffness. The tunability of the magnetic coupling also enables complete flattening of the dispersion bands. Moreover, the ground stiffness within our unit-cell design causes the opening of zero-frequency band gaps. We present our approach through a combination of analytical, numerical, and experimental methods. The analytical framework provides a blueprint for obtaining each of these exotic dispersion characteristics. The numerical analysis, using both linear and nonlinear models, validates our analytical predictions, which we further confirm through experimental demonstrations. Our work opens the door to exploring magnetic tunability and hierarchy in engineering metamaterial systems with exotic properties that can be harnessed in advanced acoustic and mechanical devices.

Inferring high-dimensional physical states from sparse, ad-hoc sensor arrays is a fundamental challenge across AI for Science and industrial IoT. Standard machine learning architectures struggle in these domains due to irregular, variable-cardinality sensor geometries and the profound sim-to-real distribution shift caused by unmodeled physical heterogeneities. To address these challenges, we propose Sensoformer, a set-attention framework integrated with Physics-Structured Domain Randomization (PSDR). By explicitly randomizing the underlying physical dynamics (e.g., propagation media, extreme noise, and network availability dropout) rather than just visual features, PSDR enforces the learning of domain-invariant physical operators. Using seismic source inversion as a rigorous real-world testbed, Sensoformer is pre-trained on 100,000 synthetics and evaluated on a highly complex real-world catalog. We demonstrate that Sensoformer achieves state-of-the-art precision and outperforms Message Passing Neural Networks (MPNNs) and Neural Operators (e.g., DeepONet) which struggle with extreme spatial sparsity and mixed-modality inputs. Furthermore, interpretability analysis reveals that the attention mechanism autonomously discovers optimal experimental design principles, dynamically prioritizing sparse orthogonal sensors to overcome information bottlenecks.

We report on the creation of $^{86}$Sr$_2$ molecules in the halo state and neighboring weakly bound states. Efficient molecule production via one-photon photoassociation relies on sufficient wavefunction overlap between the target vibrational states in the electronic excited- and ground-state potentials. Using Autler-Townes spectroscopy, transition strengths are measured to identify optimal pathways for production of weakly bound molecules. Vibrational splittings for the three least-bound vibrational states are measured, and dominant systematic uncertainties are evaluated with uncertainties below 100 Hz. From these splittings, absolute binding energies for these weakly bound vibrational states are determined. The results pave the way to a molecular isotope shift measurement with Sr$_2$.

This paper diagnoses a much-discussed problem in quantum thermodynamics, that of generalizing classical work into the quantum domain. I begin with the no-go theorem of Perarnau-Llobet et al (2017): no universal measurement scheme for quantum work satisfies two intuitive, classically consilient desiderata. I assess this incompatibility as stemming from the measurement problem. Decoherence restores compatibility for all practical purposes, but raises questions about what 'universality' should mean and whether any measurement scheme can be 'universal'. I consider a different standard of universality – in terms of ontology – by defining a trajectory-based notion of quantum work using the quantum potential. While this preserves the classical role of work as the integral of forces over distances, and evades the tension of the no-go theorem, consilience fails elsewhere; no single quantum work concept seems capable of preserving all classical features, raising questions for what it takes for successful generalization of the work concept to quantum thermodynamics.

We develop a framework for localized source detection in dynamical systems governed by nonlinear partial differential equations based on first and second-order sensitivity analysis. Building on the standard adjoint formulation, which relates multiple measurements to external sources through a linear duality relation, we first introduce a linear positional embedding that identifies the source location by aligning the measurement vector with the embedding. To capture weakly nonlinear effects that arise when the source intensity is finite, we then incorporate a quadratic correction represented as a symmetric bilinear operator and approximated via a truncated eigen-expansion obtained with Krylov subspace iterations. This yields quadratic positional embeddings that augment the linear adjoint field, enabling measurement data to be projected onto a higher-dimensional hyperplane, spanned by the linear and quadratic embeddings. A source search algorithm is formulated based on principal angle minimization between this hyperplane and the observation vector, providing a natural probabilistic interpretation of source location. The method operates in a one-shot fashion without iterative updates of candidate source positions, and it can be readily extended to scenarios involving multiple sources. Demonstrations on benchmark inverse problems include perturbation-source identification in the viscous Burgers equation and heat-source detection in a two-dimensional laminar stratified channel. The results with quadratic embeddings show significant improvements in localization accuracy compared with linear adjoint-based sensitivity methods, especially in the region where linear adjoint sensitivity vanishes.

We describe an explicit basis for the $\operatorname{SU}(2)$-invariant space of the exterior power $\wedge_{2k} \mathbb{C}^{2m}$ via the combinatorics of plane partitions. In quantum chemistry, this is the space of spin adapted quantum states of an electronic system with $m$ spin orbitals and $k$ electron pairs. We construct our basis by identifying the invariant space with an Artinian commutative ring called the excitation ring. We compute a Gröbner basis and enumerate its standard monomials via an explicit bijection to Dyck paths counted by the Narayana numbers.

We derive a framework for classifying topological bands in three-dimensional photonic band structures, where the zero frequency polarization singularity implied by Maxwell's equations complicates the direct application of existing symmetry-based approaches. Building on recent advances in the regularization of photonic bands, we use the recently introduced concept of stable real-space invariants (SRSIs) to show how photonic band structures can be unambiguously characterized in terms of equivalence classes of band representations. We classify topologically trivial photonic bands using SRSIs, treating them as the fundamental building blocks of 3D photonic band structures. This means that if certain bands cannot be constructed from these building blocks, they are necessarily topological. Furthermore, we distinguish between photonic and electronic band structures by analyzing which SRSI values are allowed in systems with and without polarization singularity. We also explore the impact of the polarization singularity on the behavior of Wilson loops, providing new insights into the topological classification of 3D photonic systems.

We investigate a minimal chase-and-escape model on a two-dimensional square lattice with randomly distributed static obstacles, focusing on how geometric disorder controls collective pursuit dynamics. Chasers and escapers move according to short-range sensing rules, while the density of obstacles tunes the connectivity of the accessible space. Using a combination of geometric analysis, dynamical observables, survival statistics, and transport characterization, we establish a direct link between lattice connectivity and pursuit efficiency. A Breadth-First Search analysis reveals that obstacle-induced fragmentation leads to a progressive loss of accessibility before the percolation threshold, defining the effective initial conditions for the dynamics. The trapping time and capture cost exhibit a non-monotonic dependence on obstacle density, reflecting a competition between path elongation in connected environments and geometric confinement near the percolation threshold. Survival analysis shows that the decay of the escaper population follows a Weibull form, with characteristic time and shape parameters displaying clear crossovers as a function of obstacle density, signaling the coexistence of cooperative capture and confinement-dominated trapping. Transport properties, quantified through the mean-squared displacement exponent, further support this picture, revealing sub-diffusive dynamics and a convergence toward a geometry-controlled regime near percolation. Overall, our results demonstrate that chase–and–escape dynamics in disordered environments are governed by a geometry-driven crossover, where percolation and connectivity act as unifying control parameters for spatial, temporal, and collective behavior.

The origin of life poses a problem of combinatorial feasibility: How can temporally supported functional organization arise in exponentially branching assembly spaces when unguided exploration behaves as a memoryless random walk? We show that nonlinear threshold-cascade dynamics in connected interaction networks provide a minimal, substrate-agnostic mechanism that can soften this obstruction. Below a critical connectivity threshold, cascades die out locally and structured input-output response mappings remain sparse and transient-a "functional desert" in which accumulation is dynamically unsupported. Near the critical percolation threshold, system-spanning cascades emerge, enabling discriminative functional responses. We illustrate this transition using a minimal toy model and generalize the argument to arbitrary networked systems. Also near criticality, cascades introduce finite-timescale structural and functional coherence, directional bias, and weak dynamical path-dependence into otherwise memoryless exploration, allowing biased accumulation. This connectivity-driven transition-functional percolation-requires only generic ingredients: interacting units, nonlinear thresholds, influence transmission, and non-zero coherence times. The mechanism does not explain specific biochemical pathways, but it identifies a necessary dynamical regime in which structured functional organization can emerge and be temporarily supported, providing a physical foundation for how combinatorial feasibility barriers can be crossed through network dynamics alone.

In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier Stokes equations. We extend the hard constraint method introduced by Chen et al. (arXiv:2012.06148) from a linear PDE to a strongly non-linear PDE. The PINN is used to estimate the stream function and pressure of the fluid, and by differentiating the stream function we can recover an incompressible velocity field. An unlearnable hard constraint projection (HCP) layer projects the predicted velocity and pressure to a hyperplane that admits only exact solutions to a discretised form of the governing equations.

The purpose of this work is to explore a teaching methodology aimed at communicating topics and subjects not typically studied and analyzed in the (Italian) secondary school. We focused specifically on the use of videogames as a recreational and educational tool, grounding our approach in a broadened conceptual view in which engagement and attentional allocation interact with motivational and affective components of play. Within this perspective, the playful format is considered not only to enhance motivation and enjoyment, but also to attenuate learners' counter-arguing tendencies when confronted with unfamiliar or abstract material. Building on this framework, we developed or adapted several videogames whose central scientific topics are phase transitions, sensitivity to initial conditions, and synchronization. We had a certain number of high school students playing the games, and we asked them several questions to guide them and determine whether the communication was successful. At the end of the activity, we administered a questionnaire about the enjoyment and the difficulties encountered in each game, and the relevant critics.

Though the performance of room-temperature masers has improved over the last decade, relatively little attention has been paid to the optics used to pump the maser's gain medium. In this work, we investigate a novel multi-blade optical “injector” that permits more effective and more scaleable pumping. The reported work encompasses an interdisciplinary mix of conceptualization, simulation, crystal growth, fabrication, and microwave engineering. Our gain medium is pentacene dissolved as a solid solution with para-terphenyl (Pc:PTP) molecular crystal. We accurately determine this pentacene's molecular absorption cross-section as a function of wavelength. Ray-tracing is then used to assess how different designs of waveguide inject light into the Pc:PTP crystal. A multi-blade injector made of high-refractive-index glass (namely Ohara S-TIH6) is predicted to pump it more completely and uniformly than previous designs. Upon hand-fabricating such an injector and Bridgman-growing a crystal of 0.1% Pc:PTP over it, an experimental maser oscillator using this combined injector-crystal assembly is demonstrated. The performance and scaleability of multiblade injection vis-a-vis alternative strategies is analyzed.

The ultrafast dynamics of azulene have been debated for decades, with reported picosecond decay constants variously attributed to intramolecular vibrational redistribution (IVR), internal conversion, or rotational dephasing. Using polarization and femtosecond time-resolved Resonance Enhanced Multi-photon Ionization Spectroscopy with a nanosecond delay window, we disentangle this long-standing inconsistency and show that the early 2-5 ps decay component arises entirely from rotational dephasing of an excited-state wavepacket. Identical time constants extracted from the decay of the parallel signal and rise of the perpendicular signal across multiple vibronic origins provide an unambiguous rotational anisotropy signature, eliminating the need for IVR-based interpretations. Extending the measurement window to 1.3 ns reveals well-structured J-type and C-type rotational coherence revivals in S2 azulene on top of the well-documented fluorescence decay, demonstrating that both the short- and long-time dynamics contain information about the coherent rotational dynamics. These results show that azulene, and by extension polycyclic aromatic hydrocarbons (PAH), can sustain structured rotational coherence deep into the nanosecond regime, positioning PAHs as model systems for quantum-coherent wavepacket dynamics and providing a framework for probing coherence, decoherence, and rotational control in electronically rich molecular systems.